Noos provides an AI‑driven platform that ingests and normalizes heterogeneous industrial sensor streams—thermal, ultrasonic, vibration, LiDAR, video, and SCADA—into a unified, time‑synchronized data model. Its multi‑modal reasoning engine detects anomalies, assigns confidence scores, and generates prescriptive, prioritized maintenance actions, which are delivered via a web dashboard and standard APIs to EAM/SCADA systems.
Funding
Funding not disclosed
Founders
Product
Problem
Industrial assets generate heterogeneous sensor streams—thermal imagery, ultrasonic scans, vibration data, LiDAR point clouds, drone video, and SCADA telemetry—but these feeds are typically siloed, requiring manual correlation and resulting in delayed fault detection and reactive maintenance. The lack of a unified data model and automated root‑cause analysis hampers operators’ ability to predict failures before they impact production.
Solution
Noos delivers an AI‑driven inspection platform that normalizes all incoming sensor streams into a single, time‑synchronized data language. A multi‑modal reasoning engine fuses visual, thermal, ultrasonic, vibration, LiDAR, and operational data to detect anomalies and infer the underlying failure mode. The system generates prescriptive alerts that include a confidence score, supporting evidence, and prioritized remediation steps, allowing operators to transition from reactive repairs to data‑backed predictive maintenance. Insights are presented through a unified web dashboard and can be pushed to existing EAM or SCADA systems via standard APIs. The platform runs in the cloud with optional edge processing for low‑latency sites, ensuring scalability across large plant footprints.
Target Audience
Primary customers are operators and maintenance teams in energy generation, oil & gas, heavy manufacturing, and large‑scale infrastructure who need predictive insight across distributed sensor networks.
Features
- Real‑time ingestion of heterogeneous streams (drones, robots, thermal cameras, ultrasonic probes, vibration sensors, LiDAR, SCADA) with automatic schema unification
- Multi‑modal AI reasoning layer that cross‑validates detections across modalities to improve root‑cause accuracy
- Confidence scoring and evidence tagging for each alert, enabling explainable AI and audit trails
- Prescriptive action recommendations with priority ranking and integration hooks for work‑order systems
- Unified, responsive dashboard that visualizes fused sensor data, anomaly timelines, and trend analytics
- RESTful and OPC‑UA compliant APIs for seamless connectivity to existing asset‑management and control platforms
- Scalable cloud architecture with optional edge compute nodes for latency‑sensitive environments
- Role‑based access control and end‑to‑end encryption to meet industrial cybersecurity standards